Emotion Recognition in Conversation
72 papers with code • 12 benchmarks • 14 datasets
Given the transcript of a conversation along with speaker information of each constituent utterance, the ERC task aims to identify the emotion of each utterance from several pre-defined emotions. Formally, given the input sequence of N number of utterances [(u1, p1), (u2, p2), . . . , (uN , pN )], where each utterance ui = [ui,1, ui,2, . . . , ui,T ] consists of T words ui,j and spoken by party pi, the task is to predict the emotion label ei of each utterance ui. .
Benchmarks
These leaderboards are used to track progress in Emotion Recognition in Conversation
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Libraries
Use these libraries to find Emotion Recognition in Conversation models and implementations
IEMOCAP
DailyDialog
MELD
EmoryNLP
SEMAINE
EmotionLines
EmoContext
CPED
CMU-MOSI
EmoWOZ
Latest papers with no code
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation
Using metric learning through a Siamese Network architecture, we achieve 57. 71 in macro F1 score for emotion classification in conversation on DailyDialog dataset, which outperforms the related work.
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause
In this paper, we propose a Unified Multimodal Emotion recognition and Emotion-Cause analysis framework (UniMEEC) to explore the causality and complementarity between emotion and emotion cause.
CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation
Emotion recognition in conversation (ERC) is a task which predicts the emotion of an utterance in the context of a conversation.
SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)
We present SemEval-2024 Task 10, a shared task centred on identifying emotions and finding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues.
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition
Emotion Recognition in Conversation (ERC) has been widely studied due to its importance in developing emotion-aware empathetic machines.
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation
MPT embeds multimodal fusion information into each attention layer of the Transformer, allowing prompt information to participate in encoding textual features and being fused with multi-level textual information to obtain better multimodal fusion features.
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement
Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work.
ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy
We utilize TE and SE to combine the strengths of previous methods in a simplistic manner to efficiently capture temporal and spatial contextual information in the conversation.
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition
On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively.
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog
How to model the context in a conversation is a central aspect and a major challenge of ERC tasks.
